基于混合自动编码器道路语义分割方法研究

Road semantic segmentation based on hybrid auto-encoder

道路检测是无人驾驶汽车环境感知的重要环节,利用计算机视觉技术实现对环境场景的语义分割是确保无人驾驶汽车安全行驶的关键技术之一。提出一种稀疏自动编码器和去噪自动编码器相结合的混合自动编码器语义分割模型,利用稀疏自动编码器的稀疏性语义编码和去噪自动编码器鲁棒的语义编码,使混合模型学习的特征更有利于图像的语义分割。通过建立一种合理的模型排列顺序与堆叠形式,实现对图像语义的优化选择,从而建立一个具有深度的“富结构”语义分割模型,进一步提高语义分割性能。实验表明,本文所提模型更为简单、训练周期短,具有较好的综合图像分割性能。

Road detection is an important part of the environment perception technology of unmanned vehicles.Using computer vision technology to achieve the semantic segmentation of environmental scenes is one of the key technologies to ensure the safe driving of unmanned vehicles.We propose a hybrid auto-encoder semantic segmentation model combining sparse auto-encoder and denoising auto-encoder.Using the sparse semantic encoding of sparse auto-encoder and the robust semantic encoding of denoising auto-encoder makes the features learned by the model more conducive for semantic segmentation.By establishing a reasonable arrangement order and stacking form of the model,an optimal selection of image semantics can be achieved,thereby creating a semantic segmentation model with deep“rich structure”,which can further improve the semantic segmentation performance.Experiments show that this model is simpler with shorter training time and better comprehensive segmentation performance.